Combined image generation of article under examination and image of test item

ABSTRACT

Among other things, one or more techniques and/or systems for generating a three-dimensional combined image is provided. A three-dimensional test image of a test item is combined with a three-dimensional article image of an article that is undergoing a radiation examination to generate the three-dimensional combined image. A first selection region of the three-dimensional article image is selected. The three-dimensional test image of the test item is inserted within the first selection region. Although the test item is not actually comprised within the article under examination, the three-dimensional combined image is intended to cause the test item to appear to be comprised within the article.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/914,035, filed Jun. 26, 2020, now U.S. Pat. No. 11,598,730, issuedMar. 7, 2023, which is a continuation of U.S. patent application Ser.No. 16/543,261, filed Aug. 16, 2019, now U.S. Pat. No. 10,697,903,issued Jun. 30, 2020, which is a continuation of U.S. patent applicationSer. No. 15/571,933, filed Nov. 6, 2017, now U.S. Pat. No. 10,401,306,issued Sep. 3, 2019, which application is a national phase entry under35 U.S.C. § 371 of International Patent Application PCT/US2015/029591,filed May 7, 2015, designating the United States of America andpublished in English as International Patent Publication WO 2016/178682A1 on Nov. 10, 2016, the disclosure of each of which is herebyincorporated herein in its entirety by this reference.

BACKGROUND

The present application relates to the field of radiation imaging. Itfinds particular application with computed-tomography (CT) securityscanners configured to generate a three-dimensional image of an articleunder examination. It also relates to medical, security, and otherapplications where the identification of articles using radiationtechnology (e.g., x-ray systems, gamma-ray systems, etc.) may be useful.

Imaging systems (e.g., also referred to as radiation imaging systems)such as computed tomography (CT) systems, diffraction CT, single-photonemission computed tomography (SPECT) systems, digital projectionsystems, and/or line systems, for example, are utilized to provideinformation, or images, of interior aspects of an article underexamination. Generally, the article is exposed to radiation comprisingphotons (e.g., such as x-ray photons, gamma ray photons, etc.), and animage(s) is formed based upon the radiation absorbed and/or attenuatedby interior aspects of the article, or rather an amount of photons thatis able to pass through the article. Generally, highly dense aspects ofthe article absorb and/or attenuate more radiation than less denseaspects, and thus an aspect having a higher density, such as a bone ormetal, for example, may be apparent when surrounded by less denseaspects, such as muscle or clothing.

Imaging systems are utilized in a variety of fields to image aspects ofan article not readily visible to the naked eye. For example, imagingsystems are used in security applications to identify potential threatitems, which may include weapons and/or explosives, concealed within asuitcase, bag, person, and/or other article, for example. Whileautomated threat detection systems are available in some imagingsystems, oftentimes it is the responsibility of an operator viewing animage of an article to determine whether the article contains apotential threat item (e.g., and thus requires additional inspections,such as a hands-on inspection). Accordingly, operators at securitycheckpoints and other venues are required to be attentive. Suchattentiveness, combined with the knowledge that few articles actuallycontain a threat item, may lead to fatigue and/or other distractionsthat potentially result in an article containing a threat item passingthrough the system undetected.

BRIEF SUMMARY

Aspects of the present application address the above matters, andothers. According to one aspect, a method for generating athree-dimensional combined image representative of an article undergoinga radiation examination and representative of a test item not comprisedwithin the article during the radiation examination is provided. Themethod comprises acquiring a three-dimensional article image of thearticle via the radiation examination and acquiring a three-dimensionaltest image of the test item. The method comprises identifying, withinthe three-dimensional article image, a first group of voxelsrepresentative of object regions corresponding to objects within thearticle and a second group of voxels representative of void regionscorresponding to voids within the article. The method comprisesselecting a first selection region of the three-dimensional articleimage within which to insert the three-dimensional test image anddetermining a degree of overlap between the first selection region andthe first group of voxels. The method comprises, when the degree ofoverlap is less than a specified degree, merging the three-dimensionaltest image with the three-dimensional article image to generate thethree-dimensional combined image, where the three-dimensional combinedimage is representative of the test item being within the article at thefirst selection region during the radiation examination. The methodcomprises, when the degree of overlap is greater than the specifieddegree, selecting a second selection region of the three-dimensionalarticle image within which to insert the three-dimensional test image.

According to another aspect, a method for generating a three-dimensionalcombined image representative of an article undergoing a radiationexamination and representative of a test item not comprised within thearticle during the radiation examination is provided. The methodcomprises acquiring a three-dimensional article image of the article viathe radiation examination and acquiring a three-dimensional test imageof the test item. The method comprises identifying, within thethree-dimensional article image, a first group of voxels representativeof object regions corresponding to objects within the article and asecond group of voxels representative of void regions corresponding tovoids within the article. The method comprises selecting a firstselection region of the three-dimensional article image within which toinsert the three-dimensional test image and determining a degree ofoverlap between the first selection region and the first group ofvoxels. The method comprises, when the degree of overlap is less than aspecified degree, determining a number of voxels within the first groupof voxels that abut the first selection region. The method comprises,when the number of voxels exceeds a threshold, merging thethree-dimensional test image with the three-dimensional article image togenerate the three-dimensional combined image, where thethree-dimensional combined image is representative of the test itembeing within the article at the first selection region during theradiation examination.

According to yet another aspect, a method for generating athree-dimensional combined image representative of an article undergoinga radiation examination and representative of a test item not comprisedwithin the article during the radiation examination is provided. Themethod comprises acquiring a three-dimensional article image of thearticle via the radiation examination and acquiring a three-dimensionaltest image of the test item. The method comprises identifying, withinthe three-dimensional article image, a first group of voxelsrepresentative of object regions corresponding to objects within thearticle and a second group of voxels representative of void regionscorresponding to voids within the article. The method comprisesselecting a first selection region of the three-dimensional articleimage within which to insert the three-dimensional test image anddetermining whether a portion of the first group of voxels are within anouter boundary region of the first selection region or an inner boundaryregion of the first selection region. The method comprises, when theportion of the first group of voxels are within the outer boundaryregion of the first selection region, merging the three-dimensional testimage with the three-dimensional article image to generate thethree-dimensional combined image, where the three-dimensional combinedimage is representative of the test item being within the article at thefirst selection region during the radiation examination.

According to yet another aspect, an imaging system is provided. Theimaging system comprises a radiation source configured to expose anarticle to radiation and a detector array configured to detect at leastsome of the radiation. The imaging system comprises an image generatorconfigured to generate a three-dimensional article image of the articlebased upon the at least some of the radiation detected by the detectorarray. The imaging system comprises an image insertion componentconfigured to identify, within the three-dimensional article image, afirst group of voxels representative of object regions corresponding toobjects within the article and a second group of voxels representativeof void regions corresponding to voids within the article. The imageinsertion component is configured to select a first selection region ofthe three-dimensional article image within which to insert athree-dimensional test image of a test item, the test item not comprisedwithin the article when the article is exposed to the radiation. Theimage insertion component is configured to determine a degree of overlapbetween the first selection region and the first group of voxels. Whenthe degree of overlap is less than a specified degree, the imageinsertion component is configured to merge the three-dimensional testimage with the three-dimensional article image to generate thethree-dimensional combined image, where the three-dimensional combinedimage is representative of the test item being within the article whenthe article is exposed to the radiation. When the degree of overlap isgreater than the specified degree, the image insertion component isconfigured to select a second selection region of the three-dimensionalarticle image within which to insert the three-dimensional test image.

Those of ordinary skill in the art may appreciate still other aspects ofthe present application upon reading and understanding the appendeddescription.

BRIEF DESCRIPTION OF THE DRAWINGS

The application is illustrated by way of example and not limitation inthe figures of the accompanying drawings, in which like referencesgenerally indicate like elements and in which:

FIG. 1 is a schematic block diagram illustrating an example environmentwhere an imaging system such as described herein may be implemented.

FIG. 2 is a flow chart diagram of an example method for generating athree-dimensional combined image representative of an article undergoingexamination and representative of a test item.

FIG. 3 illustrates an example 3D article image.

FIG. 4 illustrates an example 3D article image.

FIG. 5 illustrates an example 3D article image.

FIG. 6 illustrates an example 3D article image.

FIG. 7 illustrates an example 3D article image.

FIG. 8 illustrates an example 3D article image.

FIG. 9 is a flow chart diagram of an example method for generating athree-dimensional combined image representative of an article undergoingexamination and representative of a test item.

FIG. 10 is a flow chart diagram of an example method for generating athree-dimensional combined image representative of an article undergoingexamination and representative of a test item.

FIG. 11 is a flow chart diagram of an example method for generating athree-dimensional combined image representative of an article undergoingexamination and representative of a test item.

FIG. 12 is a flow chart diagram of an example method for generating athree-dimensional combined image representative of an article undergoingexamination and representative of a test item.

FIG. 13 illustrates an example 3D article image.

FIG. 14 illustrates an example 3D article image.

FIG. 15 is a flow chart diagram of an example method for generating athree-dimensional combined image representative of an article undergoingexamination and representative of a test item.

FIG. 16 is a flow chart diagram of an example method for generating athree-dimensional combined image representative of an article undergoingexamination and representative of a test item.

FIG. 17 is an illustration of an example computer-readable mediumcomprising processor-executable instructions configured to embody one ormore of the provisions set forth herein.

DETAILED DESCRIPTION

The claimed subject matter is now described with reference to thedrawings, wherein like reference numerals are generally used to refer tolike elements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providean understanding of the claimed subject matter. It may be evident,however, that the claimed subject matter may be practiced without thesespecific details. In other instances, structures and devices areillustrated in block diagram form in order to facilitate describing theclaimed subject matter.

Imaging systems that employ radiation (e.g., ionizing radiation such asx-rays, gamma rays, etc.) to generate images are utilized in a varietyof applications, including for security purposes within transportationnetworks and/or other sensitive areas by imaging bags, suitcases,people, etc. (e.g., collectively referred to as articles). One exampletype of such an imaging system is a CT imaging system, which isconfigured to generate three-dimensional (3D) images of articles underexamination and allows for automated and/or manual detection ofpotential threat items.

In a typical configuration, a CT imaging system presents an operatorwith 3D volumetric images and/or two-dimensional (2D) projections (e.g.,projected from the 3D volumetric images) of articles in the imagedvolume, which comprises a bin, bag, or other article conveyed through anexamination region. The system may also perform automated detection ofthreat items, which may highlight suspected threat items. The operatoris typically responsible for determining whether an additionalinspection, such as a manual inspection, of the article is warranted.

The effectiveness and/or reliability of the operator may depend upon,among other things, training, level of fatigue, and/or presence ofperformance controls configured to evaluate, control, and/or maintain anoperator's performance. Accordingly, a common approach to control and/ormaintain operator performance is randomized testing. By way of example,test bags comprising test items that appear to be potential threat itemsmay be intermingled with other bags on a conveyor belt for examinationby the imaging system, and the operator's ability to correctly identifythe test bag as containing a potential threat item may be measured.While such a technique is useful, it may be appreciated that there are alimited number of possible test bags and/or potential threat items at aparticular security checkpoint, and thus operators may becomefamiliarized with the test bags and/or potential threat items over time.

Accordingly, systems and/or techniques are described herein that providefor inserting a 3D test image of a test item (e.g., potential threatitem) into a 3D article image of an article (e.g., such as a benignsuitcase or other bag) to generate a 3D combined image. The 3D combinedimage represents both the test item and the article, and thus it appearsas though the test item is comprised within the article (e.g., eventhough the test item was not comprised within the article when thearticle underwent an examination). A data structure may comprise aplurality (e.g., 10 s, 100 s, 1000 s, etc.) of test item images, eachrepresentative of a different test item, and the 3D test image that isutilized may be selected at random, for example. In a possible example,a location and/or an orientation of the inserted test may also be atrandom. Moreover, in one embodiment, the particular article into whichthe test item is artificially inserted may be selected at random. Thus,it may be more difficult for operators to become familiarized with thearticles and/or potential threat items, for example.

The 3D combined image may be derived by combining a 3D article image ofthe article under examination with a 3D test image of the test item(e.g., the threat item). By way of example, the 3D article image of thearticle may be analyzed to identify a first selection region that issubstantially free of dense objects and/or objects having a densityand/or atomic number higher than a specified density and/or atomicnumber threshold. The 3D test image of the test item can thereafter beartificially inserted into the first selection region to generate the 3Dcombined image.

It may be appreciated that while continued reference is made herein toCT systems employed in security applications, the instant disclosure,including the scope of the claims, is not intended to be limited to suchembodiments (e.g., CT systems employed in security applications). Forexample, the systems and/or techniques provided for herein may findapplicability in medical applications and/or industrial applicationsthat utilize CT imaging systems and/or other imaging systems to generateimages. By way of example, images of tumors and/or other abnormalitiesmay be inserted into images of patients to test the ability of students,technicians, and/or doctors to identify the abnormalities.

Moreover, that instant application is not intended to be limited to usewith a particular radiation measurement technique. For example, thesystems and/or techniques described herein may find applicability tocharge-integrating imaging systems, photon counting imaging systems,single-energy imaging systems, multi-energy (dual-energy) imagingsystems, indirect conversion imaging systems, and/or direct conversionimaging systems, for example.

FIG. 1 illustrates an example environment 100 of an imaging system thatutilizes radiation to image an article as provided for herein. It may beappreciated that the example environment 100 merely provides an examplearrangement and is not intended to be interpreted in a limiting manner,such as necessarily specifying the location, inclusion, and/or relativeposition of the components depicted therein. By way of example, the dataacquisition component 122 may be part of the detector array 118. Thoughillustrated as comprising two separate structures, in a possibleexample, an image insertion component 126 may be comprised as a part ofa terminal 130.

In the example environment 100, an examination unit 102 of the imagingsystem is configured to examine articles (e.g., bags, suitcases,patients, etc.), such as an article 104. By way of example, theexamination unit 102 may be configured to examine a series of bagsplaced on a conveyor belt and conveyed through the imaging system.

The examination unit 102 can comprise a rotating gantry 106 and a(stationary) support structure 108 (e.g., which may encase and/orsurround at least a portion of the rotating gantry 106 (e.g., asillustrated with an outer, stationary ring, surrounding an outside edgeof an inner, rotating ring)). The article 104 can be placed on a supportarticle 110 of the examination unit 102, such as a bed or conveyor belt,for example, and may be conveyed or translated into an examinationregion 112 (e.g., a hollow bore in the rotating gantry 106) configuredto selectively receive the article 104. The rotating gantry 106 can berotated about the article 104 during the examination and/or movedrelative to the article 104 by a rotator 114, such as a motor, driveshaft, chain, roller truck, etc.

The rotating gantry 106 may surround a portion of the examination region112 and may comprise a radiation source 116 (e.g., an ionizing radiationsource such as an x-ray source, gamma-ray source, etc.) and a detectorarray 118 that is mounted on a substantially diametrically opposite sideof the rotating gantry 106 relative to the radiation source 116. In thisway, a relative position of the radiation source 116 and the detectorarray 118 (e.g., the position of the radiation source(s) 116 relative tothe detector array 118) may be maintained during an examination of thearticle 104, for example.

During the examination of the article 104, the radiation source 116emits fan and/or cone shaped radiation 120 from a focal spot(s) of theradiation source 116 (e.g., a region within the radiation source 116from which radiation 120 emanates) into the examination region 112. Itmay be appreciated that such radiation 120 may be emitted substantiallycontinuously and/or may be emitted intermittently or periodically (e.g.,a brief pulse of radiation is emitted followed by a resting periodduring which the radiation source 116 is not activated).

As the emitted radiation 120 traverses the article 104, the radiation120 may be attenuated differently by different aspects of the article104. Because different aspects attenuate different percentages of theradiation 120, an image(s) of the article 104 may be generated basedupon the attenuation, or variations in the number of radiation photonsthat are detected by the detector array 118. For example, more denseaspects of the article 104, such as a metal plate, may attenuate more ofthe radiation 120 (e.g., causing fewer radiation photons to strike thedetector array 118) than less dense aspects, such as clothing.

Radiation detected by the detector array 118 may be directly convertedand/or indirectly converted into analog signals that can be transmittedfrom the detector array 118 to a data acquisition component 122 operablycoupled to the detector array 118. The analog signal(s) may carryinformation indicative of the radiation detected by the detector array118 (e.g., such as an amount of charge measured over a sampling periodand/or an energy level of detected radiation), and the data acquisitioncomponent 122 may be configured to convert the analog signals intodigital signals and/or to compile signals that were transmitted within apredetermined time interval, or measurement interval, using varioustechniques (e.g., integration, photon counting, etc.).

In the example environment 100, an image generator 124 (e.g., or imagereconstructor) is configured to receive the projection data that isoutput by the data acquisition component 122. Such an image generator124 may be configured to generate one or more images of the article 104under examination from the projection data using a suitable analytical,iterative, and/or other image generation technique (e.g.,back-projection reconstruction, tomosynthesis reconstruction, iterativereconstruction, etc.). In this way, the data is converted fromprojection space to image space, a domain that may be moreunderstandable by a user 134 viewing the image(s), for example.

It may be appreciated that because the position of the radiation source116 and/or the detector array 118 change relative to the article 104during the examination (e.g., due to the rotation of the radiationsource 116 and/or detector array 118 about the article 104), volumetricdata indicative of the article 104 may be yielded from the informationgenerated by the detector array 118. Accordingly, the image(s) generatedby the image generator 124 may be 3D images (e.g., also referred to asvolumetric images), for example.

The example imaging system further comprises an image insertioncomponent 126 that may be operably coupled to the image generator 124and is configured to insert a 3D test image of a test item (e.g., anitem not comprised within the article 104 undergoing examination) into a3D article image to generate a 3D combined image. That is, stateddifferently, the image insertion component 126 is configured to combinea 3D article image of the article 104, provided by the image generator124, with a 3D test image of a test item, provided by a test item datastructure 128, to generate a 3D combined image that illustrates the testitem as comprised within the article 104. By way of example, the imageinsertion component 126 may be configured to insert a 3D test image of aweapon, explosive, or other threat item into a 3D article image of abenign bag to create a 3D combined image that appears to show a threatitem within the bag. In this way, a 3D combined image may be createdthat tests the ability of an operator to identify a potential threatitem without requiring a test bag, actually containing the threat item,to be examined, for example. In a possible example, the image generator124 may not generate full 3D images but, rather, only portions (e.g.,“slices”) of 3D images. In such an example, the image insertioncomponent 126 may not be coupled to the image generator 124 but, rather,may be coupled to a terminal 130 that assembles the images received fromthe image generator 124 and the image insertion component 126.

In the illustrated embodiment, 3D test images of one or more test itemsare stored in the test item data structure 128, which is operablycoupled to the image insertion component 126. In one embodiment, thetest item data structure 128 may comprise a plurality of 3D test imagesrespectively representative of one or more test items, and one or moreof the 3D test images stored in the test item data structure 128 may beselected for insertion into a 3D article image of the article 104. Itmay be appreciated that by having a large pool of 3D test images (e.g.,respectively representative of a different test item), it may bedifficult for a user 134 inspecting images to become accustomed to thetest items (e.g., where becoming accustomed to the test items may makeidentification of the test items easier and thus decreases the value ofthe 3D combined image as a testing tool or performance measure).

The image insertion component 126 is configured to select a 3D testimage of a test item from the test item data structure 128. Theselection of the 3D test image of the test item by the image insertioncomponent 126 may be random or may be a function of specified criteriainput into the image insertion component 126. For example, based upon apriori knowledge, it may be known that some test items (e.g., targets)and/or classes of test items are more difficult for operators to detectthan other test items. Accordingly, test images of test items may beselected by the image insertion component 126 based upon a desireddegree of difficulty, desired orientation, frequency, etc. By way ofexample, a supervisor of a user 134 may desire to test the user 134 on aparticular class of test item and/or may desire to specify a degree ofdifficulty at which to test the user 134. Based upon input from thesupervisor, the image insertion component 126 may select a test image(e.g., an image of the test item) that satisfies the specifications ofthe supervisor and may retrieve a 3D test image of the test item fromthe test item data structure 128, for example.

The image insertion component 126 is further configured to determine aselection region in the 3D article image of the article 104 into whichto insert the 3D test image of the test item. As will be furtherdescribed in more detail below, the selection region may be determinedat random and then verified (e.g., to verify that the selection regiondoes not comprise objects that would make it physically impossible toplace the test item in the selection region if the test item wasactually concealed within the article 104) and/or the selection regionmay be determined based upon an image metric, such as a CT value ofrespective voxels in the 3D image article (e.g., where the CT value isindicative of density information, z-effective information, Comptonscore, etc.). For example, in one embodiment, the image insertioncomponent 126 is configured identify one or more groups of voxels havingan image metric (e.g., such as CT value) that is below a specifiedthreshold, and to define at least one of the one or more groups ofvoxels as the selection region.

The example environment 100 further comprises a terminal 130, orworkstation (e.g., a computer), that may be configured to receive imagesgenerated by the image generator 124 and/or synthesized images generatedby the image insertion component 126. At least some of the receivedinformation/images may be provided by the terminal 130 for display on amonitor 132 to a user 134 (e.g., security personnel, medical personnel,etc.). In this way, the user 134 can inspect the image(s) to identifyareas of interest within the article 104 and/or the user 134 can betested by displaying a combined image(s), for example. The terminal 130can also be configured to receive user input which can direct operationsof the examination unit 102 (e.g., a speed to rotate, a speed anddirection of a support article 110, etc.), for example.

In the example environment 100, a controller 136 is operably coupled tothe terminal 130. The controller 136 may be configured to controloperations of the examination unit 102. By way of example, in oneembodiment, the controller 136 may be configured to receive informationfrom the terminal 130 and to issue instructions to the examination unit102 indicative of the received information (e.g., adjust a speed of aconveyor belt).

FIG. 2 illustrates an example method 200 for generating a 3D combinedimage representative of an article undergoing a radiation examinationand representative of a test item not comprised within the articleduring the radiation examination. The example method 200 begins at block202, and a 3D article image of an article is acquired from the radiationexamination at block 204. For example, in an embodiment, acomputed-tomography (CT) examination or other radiation examination maybe performed on the article and the 3D article image of the article maybe derived from the radiation examination using analytic, iterative, orother reconstruction techniques. The 3D article image of the article 104represents a volume of the article 104 and typically depicts one or moreinternal or interior aspects of the article 104. For example, where thearticle 104 under examination is baggage, the 3D article image maydepict contents of the baggage.

At block 206, in the example method 200, a 3D test image of a test itemis acquired. The test item may not be comprised within the article 104,although an end result of the example method 200 may be to produce a 3Dcombined image that makes it appear as though the test item is comprisedwithin the article 104. In security applications, the test item may be aweapon or other threat item that a user 134 is expected to be able toidentify within an image.

At block 208, in the example method 200, a first group of voxelsrepresentative of object regions corresponding to objects within thearticle 104 and a second group of voxels representative of void regionscorresponding to voids within the article 104 are identified. In anexample, the image insertion component 126 can identify voxels that havean image metric that is above or below a specified threshold and cangroup voxels based upon the image metric, where voxels having an imagemetric above the threshold are grouped in the first group and voxelshaving an image metric below the threshold are grouped in the secondgroup.

For example, the image metric can correspond to CT value, which is basedupon density information, z-effective information, or other informationderivable from projection data generated from the radiation examination.In some embodiments, the CT value for a voxel is proportional to thedensity and/or atomic number of an object represented by the voxel. Forexample, high density objects, such as a metal plate, explosive, etc.,may be represented by voxels having a higher CT value than lower densityobjects, such as empty spaces, clothing, etc., which may be representedby voxels having a lower CT value. As will be further appreciated below,the specified threshold may be selected based upon what types of objectsare to be effectively considered a void and what types of objects are tobe considered objects that the threat item cannot overlap without makingthe combined image appear unrealistic (e.g., a gun occupying a samespace as a heel of a shoe may appear unrealistic, thus making the gunmore easily detectable). In some embodiments, the threshold is selectedto treat voxels representative of clothing as void regions. In otherembodiments, the threshold is selected to treat voxels representative ofclothing as object regions.

At block 210, in the example method 200, a first selection region of the3D article image, within which to insert the 3D test image, is selected.In an example, the image insertion component 126 can select the firstselection region of the 3D article image at random without informationregarding image metrics associated with the 3D article image and/orwithout information regarding the test image.

In another example, the image insertion component 126 can select thefirst selection region of the 3D article image based on specifiedcriteria. By way of example, the image insertion component 126 canselect the first selection region based on image metrics associated with3D article image. As an example, the image insertion component 126 canidentify clusters of voxels that correspond to the second group ofvoxels, which are representative of void regions. The first selectionregion can then be defined (e.g., selected) based upon one or more ofthese clusters of voxels. In some embodiments, defining the firstselection region comprises defining the first selection region as afunction of the size and/or shape of the test image of the test item(e.g., to select a cluster of voxels that best approximates the sizeand/or shape of the test item). In other embodiments, the firstselection region can be defined irrespective of the size and/or shape ofthe test image. For example, the first selection region can be definedas a largest cluster of voxels that correspond to the second group ofvoxels.

At block 212, in the example method 200, a degree of overlap between thefirst selection region and the first group of voxels is determined. Inan example, after the first selection region has been selected, theimage insertion component 126 can determine whether the first selectionregion overlaps the first group of voxels, which are representative ofobject regions corresponding to objects having a density above a definedthreshold, for example. In some examples, the degree of overlaprepresents a number of voxels of the first group of voxels that areoverlapped by the first selection region.

At block 214, in the example method 200, when the degree of overlap isless than a specified degree (e.g., when the total number of voxelswithin the first selection region that are associated with the firstgroup is less than a specified number and/or a percentage of voxelswithin the first selection region that are associated with the firstgroup is less than a specified percentage), the 3D test image is mergedwith the 3D article image to generate the 3D combined image, where the3D combined image is indicative of the test item being within thearticle 104 at the first selection region during the radiationexamination.

In an example, voxels of the 3D article image of the article 104 withinthe first selection region may be replaced with voxels of the 3D testimage of the test item to artificially insert the test item into thearticle 104. In another example, instead of replacing the voxels of the3D article image of the article 104, one or more properties of suchvoxels within the first selection region may be combined with one ormore corresponding properties of voxels of the 3D test image of the testitem. For example, CT values of one or more voxels of the 3D articleimage of the article 104 within the first selection region may becombined (e.g., summed) with CT values of one or more voxels of the 3Dtest image of the test item.

At block 216, in the example method 200, when the degree of overlap isgreater than the specified degree, a second selection region of the 3Darticle image within which to insert the 3D test image can be selected.In such an example, the second selection region can be selected in asimilar manner as the first selection region was selected. That is, thesecond selection region can be selected at random or may be selected asa function of specified criteria. The example method 200 ends at block218.

FIGS. 3 to 8 provide example illustrations of various 3D images atdifferent stages of the image insertion process. With respect to FIG. 3, an example 3D test image 300 of a test item 302 is illustrated. Itwill be appreciated that in this example, the 3D test image 300 definesa bounding box (e.g., boundary) of the test item 302. The 3D test image300 and the test item 302 are not so limited, and in another example,the 3D test image 300 may mimic the size of the test item 302 but notthe shape, such as by having a substantially rectangular shape, ovalshape, or the like. The 3D test image 300 may be retrieved from the testitem data structure 128 (e.g., illustrated in FIG. 1 ) by the imageinsertion component 126. In the illustrated example, the test item 302is a gun, although other threat items and/or non-threat items are alsocontemplated. In some examples, such as the example illustrated in FIG.3 , the 3D test image 300 can represent more than just the test item302. That is, the 3D test image 300 can represent voxels notrepresentative of the gun, which may be zeroed-out (such that the voxelsare essentially devoid of information and thus represent empty space).In this example, the 3D test image 300 has a cubic shape. In otherexamples, however, the 3D test image 300 can have a shape that moreclosely matches a shape of the test item 302, such as by having a gunshape to match the test item 302 (e.g., a gun) in FIG. 3 .

FIG. 4 illustrates an example 3D article image 400 of an article 402(e.g., such as a suitcase) that underwent and/or is presently undergoinga radiation examination. Such a 3D article image 400 may be generated bythe image generator 124 and/or obtained by the image insertion component126, for example. The article 402 comprises one or more object regions404 corresponding to objects 408, 410 within the article 402. Thearticle 402 comprises one or more void regions 412 corresponding tovoids 414 within the article 402 and/or corresponding to portions of thearticle 402 having an image metric (e.g., CT value) less than thespecified threshold. The objects 408, 410 have any number of sizes andshapes. In the illustrated example, a first object 408 is substantiallyhexahedron-shaped while a second object 410 is substantiallycylindrically shaped. The void regions 412 comprise substantially emptyspace and/or low-attenuation materials, such as clothing, for example.

FIG. 5 illustrates the 3D article image 400 of the article 402 after afirst selection region 504 has been selected. In this example, the firstselection region 504 is located within the void region 412 of the 3Darticle image 400. Such a position is not intended to be limiting,however. Rather, in other examples, the first selection region 504 canpartially or completely overlap with one or more of the objects 408, 410of the object regions 404. In addition, in the illustrated example, thefirst selection region 504 has a cubic shape though, in other examples,the first selection region 504 can have a shape that more closelymatches a shape of the test item 302, such as by having a gun shape, forexample. In the illustrated example, a degree of overlap between thefirst selection region 504 and a first group of voxels representative ofthe object regions 404 corresponding to the objects 408, 410 can beidentified.

If the degree of overlap between the first selection region 504 and thefirst group of voxels is less than a specified degree, the 3D test image300 may be merged with the 3D article image 400 to form the 3D combinedimage 600 as shown in FIG. 6 .

In some embodiments, the location of voxels of the first group are alsoconsidered when determining whether a degree of overlap precludes theselection region as a possible location for inserting the test item. Byway of example, in some embodiments, when a 3D test image 300 is mergedwith a 3D article image 400, a CT value for the 3D combined image isbased upon a weighted average of a CT value of a voxel of the 3D articleimage 400 and a corresponding CT value of a voxel of the 3D test image.In some embodiments, a weight applied to the CT value of the voxel ofthe 3D article image 400 relative to a weight applied to thecorresponding CT value of the voxel of the 3D test image may be afunction of the location of respective voxels relative to the selectionregion. For example, a higher relative weight may be applied to voxelsof the 3D article image 400 than to voxels of the 3D test image 300 nearan outer boundary of the first selection region 504, whereas a higherrelative weight may be applied to voxels of the 3D test image 300 thanvoxels of the 3D article image 400 near an inner core of the firstselection region 504. Thus, a plurality of voxels of the first groupthat are located near an outer boundary of the first selection region504 may be able to be blended better in the 3D combined image 600 than aplurality of voxels of the first group that are located near an innercore of the first selection region 504.

Referring to FIG. 7 , a first selection region 704 may be defined thatcomprises an outer boundary region 706 and an inner boundary region 708.The first selection region 704 may overlap a portion of the first groupof voxels representative of the object regions 404 corresponding to theobjects 408, 410. In this example, the first selection region 704overlaps the second object 410, but, in other examples, the firstselection region 704 can overlap either the first object 408 or thesecond object 410, both the first object 408 and the second object 410,neither the first object 408 nor the second object 410, or other,un-illustrated objects. In this example, a degree of overlap between thefirst selection region 704 and the first group of voxels can bedetermined.

In some examples, the determination of the degree of overlap between thefirst selection region 704 and the first group of voxels comprisesdetermining whether a portion of the first group of voxels that arewithin the first selection region 704 are within an inner boundaryregion 708 of the first selection region 704. In an example, when theportion of the first group of voxels that are within the inner boundaryregion 708 of the first selection region 704 does not exceed an innerboundary threshold, the 3D test image 300 can be merged with the 3Darticle image 400 to form the 3D combined image, such as by insertingthe 3D test image 300 into the first selection region 704. As such, thetest item 302 of the 3D test image 300 may not overlap the first groupof voxels representative of the object regions 404. Thus, the degree ofoverlap between the first selection region 704 and voxels of the firstgroup does not necessarily preclude the 3D test image 300 from beingmerged with the 3D article image 400 at the selection region if theoverlap occurs within an outer boundary region 706 and/or occurs, to alesser extent, within an inner boundary region 708. In an example, theinner boundary region 708 can be defined as a region within the firstselection region 704 that substantially matches a size and/or a shape ofthe test item 302. In the illustrated example, the inner boundary region708 substantially mimics the shape and/or boundary of the test item 302(e.g., by having a gun shape) while in other examples, the innerboundary region 708 may mimic the size of the test item 302 but not theshape, such as by having a substantially rectangular shape, oval shape,or the like. In an example, the outer boundary region 706 can be definedas a region within the first selection region 704 that forms a perimeterpartially or completely around the inner boundary region 708. In someexamples, the outer boundary region 706 may comprise artifacts relatedto the test item 302. Voxels within the inner boundary region (e.g.,representative of the test item) and voxels within the outer boundaryregion 706 (e.g., representative of artifacts) are both combined withthe voxels of the 3D article image 400, in some embodiments, to enablethe test item 302 to better assimilate within the 3D article image 400(e.g., making the test item 302 appear as though it was present withinthe article during the examination of the article).

When the portion of the first group of voxels that are within the innerboundary region 708 of the first selection region 704 exceeds the innerboundary threshold, the 3D test image 300 may not be merged with the 3Darticle image 400 because of a potentially noticeable mismatch betweenthe test item 302 and the portion of the first group of voxels (e.g.,the second object 410), which increases a possibility of the operatordetecting the presence of an image manipulation.

FIG. 8 illustrates the 3D article image 400 of the article 402 after thesecond selection region 804 has been selected because the degree ofoverlap (illustrated in FIG. 7 ) between the first selection region 704and the first group of voxels is greater than the specified degree andbecause of the location of the voxels of the first group relative to thefirst selection region (e.g., the number of voxels within the innerboundary region 708 exceeds an inner boundary threshold). In an example,the second selection region 804 can be selected in a similar manner asdescribed above with respect to the first selection region 504, 704.That is, the second selection region 804 can be selected at random orbased on specified criteria.

FIG. 9 is a flow diagram illustrating an example embodiment 900 ofgenerating a 3D combined image representative of an article undergoing aradiation examination and representative of a test item not comprisedwithin the article during the radiation examination. The exampleembodiment 900 begins at block 902. At block 904, a 3D article image(e.g., the 3D article image 400 illustrated in FIG. 4 ) is acquired.

At block 906, a 3D test image (e.g., 3D test image 300 illustrated inFIG. 3 ) of a test item (e.g., test item 302) is acquired. In anexample, the 3D test image can be acquired from a test item datastructure (e.g., test item data structure 128 illustrated in FIG. 1 ).In some examples, the test item comprises a threat item.

At block 908, a first group of voxels representative of object regionsand a second group of voxels representative of void regions areidentified. In an example, the identification comprises selecting aspecified threshold for object regions and void regions within the 3Darticle image. The first group of voxels, representative of the objectregions (e.g., object regions 404 illustrated in FIG. 4 ), can have animage metric that is above the specified threshold. The second group ofvoxels, representative of the void regions (e.g., void region 412illustrated in FIG. 4 ), can have an image metric that is below thespecified threshold. According to some examples, the image metric can bebased on density information or other information (e.g., z-effective,Compton score, etc.).

At block 910, a first selection region of the 3D article image can beselected. The first selection region (e.g., first selection region 504illustrated in FIG. 5 ) can be selected at random and/or based uponspecified criteria. In some examples, the first selection regioncomprises an outer boundary region (e.g., outer boundary region 706illustrated in FIG. 7 ) and an inner boundary region (e.g., innerboundary region 708).

At block 912, in an example, the 3D test image can be inserted withinthe first selection region. It will be appreciated that in someexamples, the 3D test image can be inserted within the first selectionregion following the selection of the first selection region, asillustrated in FIG. 9 . In other examples, the 3D test image may not beinserted within the first selection region until later (e.g., afterdetermining a degree of overlap between the first selection region andthe first group of voxels).

At block 914, the example embodiment 900 comprises determining whetherthe first selection region lies on the second group of voxels. In someexamples, a majority of the first selection region may lie on (e.g.,overlap) the second group of voxels. In such an example, since thesecond group of voxels are representative of a void region, the firstselection region may lie on a void region.

FIG. 10 is a flow diagram illustrating an example embodiment 1000 thatfollows the example embodiment 900 illustrated in FIG. 9 . In thisexample, the example embodiment 1000 can follow the determination atblock 914 of whether the first selection region lies on (e.g., overlaps)the second group of voxels. The first selection region may at leastpartially lie on the first group of voxels. In such an example, thefirst selection region may not lie on (e.g., completely overlap) thesecond group of voxels (e.g., NO at block 914).

At block 1002, the example embodiment 1000 comprises determining whetherthe overlap between the first selection region and the first group ofvoxels is less than a specified degree. In an example, when the overlapis less than the specified degree (e.g., YES at block 1002), the 3D testimage of the test item can be merged with the 3D article image. In thisexample, the 3D test image of the test item can be inserted into thefirst selection region. Voxels of the 3D test image, comprising the testitem, can replace voxels of the 3D article image at the location of thefirst selection region so as to artificially insert the 3D test imageinto the 3D article image. In another embodiment, the 3D test image ofthe test item may be overlaid on top of the 3D article image at thelocation of the first selection region (e.g., merging, summing,averaging, etc., voxels of the 3D test image with voxels of the 3Darticle image).

In some examples, overlapping voxels of the 3D test image that overlapthe first group of voxels can be weighted. In such an example, theseoverlapping voxels (e.g., of the 3D test image) can be weighted with theportion(s) of the first group of voxels (e.g., overlapped voxels) ratherthan replacing the portion of the first group of voxels. For example,one or more properties of these overlapping voxels of the 3D test imagecan be combined with one or more corresponding properties of theportion(s) of the first group of voxels that is overlapped. In anexample, CT values of the 3D test image may be combined (e.g., summed)with CT values of the portion of the first group of voxels that areoverlapped.

At block 1006, when the overlap is greater than the specified degree(e.g., NO at block 1002), the example embodiment 1000 comprisesdetermining whether the portion of the first group of voxels that isoverlapped by the first selection region is within the inner boundaryregion (e.g., inner boundary region 708) of the first selection region.In such an example, the first selection region 704 has the innerboundary region and an outer boundary region (e.g., outer boundaryregion 706). When the 3D test image is inserted into the first selectionregion, the outer boundary region generally corresponds to a void regionwhile the inner boundary region generally corresponds to the test item.If the portion of the first group of voxels is not within the innerboundary region (e.g., NO at block 1006), then the 3D test image of thetest item can be merged with the 3D article image (e.g., at block 1004).

At block 1008, if the portion of the first group of voxels is within theinner boundary region of the first selection region (e.g., YES at block1006), then a second selection region (e.g., second selection region 804illustrated in FIG. 8 ) is selected. Once the second selection regionhas been selected, the method can begin again at block 912 in FIG. 9 .

FIG. 11 is a flow diagram illustrating an example embodiment 1100 thatfollows block 914 illustrated in FIG. 10 . In this example, the exampleembodiment 1100 can follow the determination that the first selectionregion lies on the second group of voxels (e.g., YES at block 914 inFIG. 10 ) representative of void regions corresponding to voids withinthe article.

At block 1102, the example embodiment 1100 comprises calculating adistance from a boundary of a test item 1304 to a nearest object belowthe boundary of the test item 1304. Referring to FIG. 13 , an example 3Darticle image 1300 is illustrated. The example 3D article image 1300comprises a 3D test image 1302 of a test item 1304. The 3D test image1302 is inserted within a first selection region 1306. A distance can becalculated from the boundary of the test item 1304 to the nearest objectbelow the first selection region 1306. In this example, the nearestobject below the first selection region 1306 is the first object 408. Inan example, the nearest object below the first selection region 1306 maycomprise a part of the article (e.g., a test bag), such as an inner wallor surface of the test bag, within which the 3D article image 1300 isinserted or may comprise an object disposed within the article (e.g.,clothing, books, etc.). Moreover, in some examples, the boundaryreferred to herein may be a lower boundary or gravitational bottom ofthe test item 1304 that is nearest to a source of attraction (e.g., asource of the gravitational pull) so as to provide an appearance of agravitationally stable resting location. Such a gravitational bottom maybe dependent upon the placement of the test item 1304 within the 3Darticle image 1300 and the orientation of the article during theexamination, for example.

At block 1104, the example embodiment 1100 comprises adjusting a y-valueof the test item 1304 to adjust a distance between the test item 1304and the nearest object (e.g., to make it appear as though the test itemis resting on the object to account for gravity). As illustrated in FIG.13 , the test item 1304 is located a distance from the nearest object(e.g., the first object 408). To adjust this distance, the y-value ofthe test item 1304 can be adjusted. As such, a position of the test item1304 can be adjusted with respect to the nearest object (e.g., the firstobject 408), with the test item 1304 being adjusted downwardly towardsthe first object 408. In such an example, a distance between the testitem 1304 and the nearest object in the y-direction is reduced, and maybe zero.

At block 1106, the example embodiment 1100 comprises adjusting anx-value of the test item 1304 to adjust a distance between the test item1304 and the nearest object (e.g., to further conceal the test item).Referring to FIGS. 13 and 14 , the first selection region 1306,comprising the 3D test image 1302 of the test item 1304, can be locateda distance from the nearest object (e.g., the second object 410) alongthe x-direction. To adjust this distance, the x-value of the test item1304 can be adjusted such that the 3D test image 1302 is located abovethe nearest object. In this example, the test item 1304 is adjustedalong the x-direction (e.g., left and right directions in FIGS. 13 and14 ). As such, a distance between the test item 1304 and the nearestobject in the x-direction is reduced, and may be zero such that the testitem 1304 may be in contact with, abutting against, etc., the nearestobject.

At block 1108, the example embodiment 1100 comprises determining anumber of voxels within the first group of voxels that abut a boundaryof the test item 1304. It is to be appreciated that the boundary of thetest item 1304 may, in some examples, comprise the lower boundary (e.g.,the gravitational bottom of the test item 1304), and/or a side boundary.Referring to FIG. 14 , after adjusting the y-value and x-value of thetest item 1304, the test item 1304 can abut (e.g., contact) the firstgroup of voxels representative of the object regions 404 correspondingto the objects 408, 410 (e.g., to make it appear as though the test itemis resting upon other objects 408, 410 (e.g., as opposed to floating inspace)). In this example, the voxels within the first group of voxelsthat abut a boundary of the test item 1304 are illustrated as abutmentlocations 1400.

FIG. 12 is a flow diagram illustrating an example embodiment 1200 thatfollows the example embodiment 1100 illustrated in FIG. 11 . In thisexample, the example embodiment 1100 can follow the determination atblock 1108 of the number of voxels within the first group of voxels thatabut the boundary of the test item 1304.

At block 1202, the example embodiment 1200 comprises determining whetherthe number of voxels within the first group of voxels that abut theboundary of the test item 1304 is greater than a threshold. Thethreshold comprises any number of abutment locations 1400 between thefirst group of voxels and the boundary of the test item 1304. In anexample, the threshold comprises three or more abutment locations 1400.Determining the number of abutment locations 1400 provides for a 3Dcombined image that is more realistic. More particularly, in an examplewhen there are zero abutment locations 1400, the first selection region1306, and, thus, the test item 1304, is located within the void region412, such that the first selection region 1306 (and, thus, the 3D testimage 300 of the test item 1304) does not contact the object regions404. Such a location for the test item 1304 may not be realistic, as thetest item 1304 would normally be supported on one or more objects 408,410. Accordingly, ensuring that a minimum number (e.g., threshold) ofabutment locations 1400 are present reduces a possibility of theoperator detecting the presence of an image manipulation.

At block 1204, when the number of voxels within the first group ofvoxels that abut the boundary of the test item 1304 is greater than thethreshold (e.g., YES at block 1202), the example embodiment 1200comprises determining whether the abutment locations 1400 are evenlydistributed around a center of mass of the test item 1304. Despite thethreshold number of abutment locations 1400 being met, the position ofthe abutment locations 1400 with respect to the first selection region1306 and the test item 1304 can indicate whether the 3D combined imageis realistic. In an example, the abutment locations 1400 may not beevenly distributed around the center of mass of the test item 1304, suchas by being concentrated at a single location (e.g., bottom corner) ofthe test item 1304. In such an example, it may not be realistic for thetest item 1304 to be supported on the object(s) 408, 410, as the testitem 1304 would likely fall over and/or be unable to maintain such aposition.

On the other hand, when the abutment locations 1400 are evenlydistributed around the center of mass of the test item 1304, such as inthe example of FIG. 14 , it is more likely for the test item 1304 tomaintain such a position, as the test item 1304 is adequately supportedabout its center of mass. Accordingly, ensuring that a relatively evendistribution of abutment locations 1400 about the center of mass of thetest item 1304 reduces a possibility of the operator detecting thepresence of an image manipulation. When the abutment locations 1400 areevenly distributed around a center of mass of the test item 1304 (e.g.,YES at block 1204), the example embodiment 1200 comprises merging the 3Dtest image 1302 with the 3D article image (e.g., at block 1206).

At block 1208, when the number of voxels within the first group ofvoxels that abut the boundary of the test item 1304 is less than thethreshold (e.g., NO at block 1202) or the abutment locations 1400 arenot evenly distributed around the center of mass of the test item 1304(e.g., NO at block 1204), the example embodiment 1200 comprisesadjusting a z-value of the test item 1304 to adjust a distance betweenthe test item 1304 and the nearest object. Referring to FIGS. 13 and 14, the first selection region 1306, comprising the 3D test image 1302,can be located a distance from the nearest object along the z-direction.To adjust this distance, the z-value of the test item 1304 can beadjusted such that the 3D test image 1302 is located above and/or incontact with the nearest object. In this example, the test item 1304 isadjusted along the z-direction (e.g., into and out of the page in FIGS.13 and 14 ). As such, a distance between the test item 1304 and thenearest object in the z-direction is reduced, and may be zero.

At block 1210, the example embodiment 1200 comprises determining whetherthe number of voxels within the first group of voxels that abut the testitem 1304 is greater than a threshold. As described above with respectto block 1202, the threshold comprises any number of (e.g., two or more)abutment locations 1400 between the first group of voxels and theboundary of the test item 1304. When the number of voxels within thefirst group of voxels that abut the boundary of the test item 1304 isgreater than the threshold (e.g., YES at block 1210), the exampleembodiment 1200 comprises determining (e.g., at block 1204) whether theabutment locations 1400 are evenly distributed around a center of massof the test item 1304. At block 1212, when the number of voxels withinthe first group of voxels that abut the test item 1304 is less than thethreshold (e.g., NO at block 1210), then a second selection region isselected.

FIG. 15 illustrates an example method 1500 for generating a 3D combinedimage representative of an article undergoing a radiation examinationand representative of a test item not comprised within the articleduring the radiation examination. The example method 1500 begins atblock 1502. At block 1504, a 3D article image of an article is acquiredvia the radiation examination. In an example, a computed-tomography (CT)examination or other radiation examination may be performed on thearticle and the 3D article image of the article may be derived from theradiation examination using analytic, iterative, or other reconstructiontechniques.

At block 1506, in the example method 1500, a 3D test image of a testitem is acquired. The test item may not be comprised within the article104, although an end result of the example method 1500 may be to producea 3D combined image that appears to illustrate that the test item iscomprised within the article 104.

At block 1508, in the example method 1500, a first group of voxelsrepresentative of object regions corresponding to objects within thearticle 104 and a second group of voxels representative of void regionscorresponding to voids within the article 104 are identified. In anexample, the image insertion component 126 can identify voxels that havean image metric that is above or below a specified threshold. The imagemetric, such as CT values, can be based on density information or otherinformation (e.g., z-effective, Compton score, etc.) derivable from the3D article image of the article 104. The first group of voxels,representative of object regions corresponding to objects, has an imagemetric that is above the specified threshold. The second group ofvoxels, representative of void regions corresponding to voids, has animage metric that is below the specified threshold.

At block 1510, in the example method 1500, a first selection region ofthe 3D article image within which to insert the 3D test image isselected. In an example, the image insertion component 126 can selectthe first selection region of the 3D article image at random or based onspecified criteria.

At block 1512, in the example method 1500, a degree of overlap betweenthe first selection region and the first group of voxels is determined.In an example, after the first selection region has been selected, theimage insertion component 126 can determine whether the first selectionregion overlaps the first group of voxels, which are representative ofthe object regions corresponding to objects.

At block 1514, in the example method 1500, when the degree of overlap isless than a specified degree, a number of voxels within the first groupof voxels that abut the test item 1304 can be determined. In an example,the number of voxels within the first group of voxels that abut the testitem 1304 comprise one or more abutment locations 1400. That is, theabutment locations 1400 represent locations in which the first group ofvoxels abut (e.g., contact) the test item 1304. In this example, thefirst group of voxels are representative of object regions correspondingto objects within the article. Accordingly, an abutment location 1400may be representative of the test item 1304 (e.g., when inserted intothe first selection region) abutting one or more of the objects, suchthat the test item 1304 is supported by one or more of the objects.

At block 1516, in the example method 1500, when the number of voxelsthat abut the test item 1304 exceeds a threshold, the 3D test image canbe merged with the 3D article image to generate the 3D combined image,where the 3D combined image is representative of the test item beingwithin the article at the first selection region during radiationexamination. The threshold comprises any number (e.g., two or more) ofabutment locations 1400 between the first group of voxels and the testitem 1304. In an example, the threshold comprises three or more abutmentlocations 1400. As such, when the number of voxels exceeds thisthreshold (e.g., three abutment locations 1400 or points of contact),the 3D test image is merged with the 3D article image, such as byinserting the 3D test image into the first selection region. The methodends at block 1518.

FIG. 16 illustrates an example method 1600 for generating a 3D combinedimage representative of an article undergoing a radiation examinationand representative of a test item not comprised within the articleduring the radiation examination. The example method 1600 begins atblock 1602. At block 1604, a 3D article image of an article is acquiredvia the radiation examination. In an example, a computed-tomography (CT)examination or other radiation examination may be performed on thearticle and the 3D article image of the article may be derived from theradiation examination using analytic, iterative, or other reconstructiontechniques.

At block 1606, in the example method 1600, a 3D test image of a testitem is acquired. The test item may not be comprised within the article104, although an end result of the example method 1600 may be to producea 3D combined image that appears to illustrate that the test item iscomprised within the article 104.

At block 1608, in the example method 1600, a first group of voxelsrepresentative of object regions corresponding to objects within thearticle 104 and a second group of voxels representative of void regionscorresponding to voids within the article 104 are identified. The firstgroup of voxels, representative of object regions corresponding toobjects, has an image metric that is above the specified threshold. Thesecond group of voxels, representative of void regions corresponding tovoids, has an image metric that is below the specified threshold.

At block 1610, in the example method 1600, a first selection region ofthe 3D article image within which to insert the 3D test image isselected. In an example, the image insertion component 126 can selectthe first selection region of the 3D article image at random or based onspecified criteria.

At block 1612, the example method 1600 comprises determining whether aportion of the first group of voxels are within an outer boundary regionof the first selection region or an inner boundary region of the firstselection region. In some examples, the inner boundary region cancorrespond to a shape of the test item 302 while the outer boundaryregion corresponds to a void region surround the test item 302.

At block 1614, when a portion of the first group of voxels are withinthe outer boundary region of the first selection region, the examplemethod 1600 comprises merging the 3D test image with the 3D articleimage to generate a 3D combined image, where the 3D combined image isrepresentative of the test item being within the article at the firstselection region during the radiation examination. In an example, whenthe portion of the first group of voxels are within the outer boundaryregion and not the inner boundary region, the 3D test image is mergedwith the 3D article image to form the 3D combined image. The examplemethod 1600 ends at block 1616.

Still another embodiment involves a computer-readable medium comprisingprocessor-executable instructions configured to implement one or more ofthe techniques presented herein. An example computer-readable mediumthat may be devised in these ways is illustrated in FIG. 17 , whereinthe embodiment 1700 comprises a computer-readable medium 1702 (e.g., aflash drive, CD-R, DVD-R, application-specific integrated circuit(ASIC), field-programmable gate array (FPGA), a platter of a hard diskdrive, etc.), on which is encoded computer-readable data 1704. Thiscomputer-readable data 1704 in turn comprises a set ofprocessor-executable instructions 1706 configured to operate accordingto one or more of the principles set forth herein. In an embodiment1700, the processor-executable instructions 1706 may be configured toperform a method 1708, such as at least some of the example methods 200of FIG. 10, 1500 of FIGS. 15 , and/or 1600 of FIG. 16 , for example. Inanother such embodiment, the processor-executable instructions 1706 maybe configured to implement a system, such as at least some of theexemplary environment 100 of FIG. 1 . Many such computer-readable mediamay be devised by those of ordinary skill in the art that are configuredto operate in accordance with one or more of the techniques presentedherein.

Moreover, “exemplary” is used herein to mean serving as an example,instance, illustration, etc., and not necessarily as advantageous. Asused in this application, “or” is intended to mean an inclusive “or”rather than an exclusive “or.” In addition, “a” and “an” as used in thisapplication are generally construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform. Also, at least one of A and B and/or the like generally means A orB or both A and B. Furthermore, to the extent that “includes,” “having,”“has,” “with,” or variants thereof are used in either the detaileddescription or the claims, such terms are intended to be inclusive in amanner similar to the term “comprising.”

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

As used in this application, the terms “component,” “module,” “system,”“interface,” and the like are generally intended to refer to acomputer-related entity, either hardware, a combination of hardware andsoftware, software, or software in execution. For example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration, both an application runningon a controller and the controller can be a component. One or morecomponents may reside within a process and/or thread of execution and acomponent may be localized on one computer and/or distributed betweentwo or more computers.

Furthermore, the claimed subject matter may be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. Of course, those skilled inthe art will recognize many modifications may be made to thisconfiguration without departing from the scope or spirit of the claimedsubject matter.

Further, unless specified otherwise, “first,” “second,” and/or the likeare not intended to imply a temporal aspect, a spatial aspect, anordering, etc. Rather, such terms are merely used as identifiers, names,etc., for features, elements, items, etc. (e.g., “a first channel and asecond channel” generally corresponds to “channel A and channel B” ortwo different channels).

Although the disclosure has been shown and described with respect to oneor more implementations, equivalent alterations and modifications willoccur to others skilled in the art based upon a reading andunderstanding of this specification and the annexed drawings. Thedisclosure includes all such modifications and alterations and islimited only by the scope of the following claims. In particular regardto the various functions performed by the above described components(e.g., elements, resources, etc.), the terms used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure which performs thefunction in the herein illustrated example implementations of thedisclosure. Similarly, illustrated ordering(s) of acts is not meant tobe limiting, such that different orderings comprising the same ofdifferent (e.g., numbers) of acts are intended to fall within the scopeof the instant disclosure. In addition, while a particular feature ofthe disclosure may have been disclosed with respect to only one ofseveral implementations, such feature may be combined with one or moreother features of the other implementations as may be desired andadvantageous for any given or particular application.

What is claimed is: 1.-24. (canceled)
 25. A method of combining an imageof a threat and an image of an article, the method comprising: acquiringa first image representative of an article examined via radiation;acquiring a second image representative of a threat, wherein the threatis not represented in the first image; identifying, via one or morehardware processors, one or more regions of the first image, wherein theone or more regions comprise one or more of: one or more object regionscorresponding to one or more objects, and one or more void regionscorresponding to one or more voids; obtaining, via the one or morehardware processors, a selection region of the first image at which toinsert the threat; processing the first image in the projection domainto obtain a property of the first image; processing the second image inthe projection domain to obtain a property of the second image; merging,via the one or more hardware processors, the second image with a portionof the first image that is within the selection region, the mergingcomprising: combining, in the projection domain, the property of thefirst image with the property of the second image; and obtaining, viathe one or more hardware processors, a merged image representative ofthe article including the threat.
 26. (canceled)
 27. The method of claim25, wherein processing the first image in the projection domain toobtain the property of the first image comprises: processing the portionof the first image that is within the selection region to obtain theproperty of the first image.
 28. The method of claim 25, whereinprocessing the first image in the projection domain to obtain theproperty of the first image comprises: processing the first image toobtain projection data that corresponds to the first image; andprocessing the projection data to obtain the property of the firstimage.
 29. (canceled)
 30. The method of claim 25, wherein combining theproperty of the first image with the property of the second imagecomprises: Combining, in the projection domain, an image metric of thefirst image with an image metric of the second image.
 31. The method ofclaim 30, wherein the combining, in the projection domain, the imagemetric of the first image with the image metric of the second imagecomprises: Combining, in the projection domain, a metric of the firstimage that is associated with density with a metric of the second imagethat is associated with density.
 32. The method of claim 30, wherein thecombining the image metric of the first image with the image metric ofthe second image comprises: Combining, in the projection domain, ametric of the first image associated with a number of photons collectedover time with a metric of the second image associated with a number ofphotons collected over time.
 33. The method of claim 30, wherein thecombining the image metric of the first image with the image metric ofthe second image comprises: combining, in the projection domain, ametric of the first image associated with one or more changes in energylevels over time with a metric of the second image associated with oneor more changes in energy levels over time.
 34. A system, comprising: atleast one processor; and a memory to store instructions, wherein theinstructions upon execution by the at least one processor, to enable atleast one hardware processor to: acquire a first image representative ofan article examined via radiation; acquire a second image representativeof a threat, wherein the threat is not represented in the first image;identify one or more regions of the first image, wherein the one or moreregions comprise one or more of: one or more object regionscorresponding to one or more objects, and one or more void regionscorresponding to one or more voids; obtain a selection region of thefirst image at which to insert the threat; process the first image inthe projection domain to obtain a property of the first image; processthe second image in the projection domain to obtain a property of thesecond image; merge the second image with a portion of the first imagethat is within the selection region, the merging comprising combining,in the projection domain, the property of the first image with theproperty of the second image; and obtain a merged image representativeof the article including the threat.
 35. The system of claim 34, whereinprocessing the first image in the projection domain to obtain theproperty comprises: processing the portion of the first image that iswithin the selection region to obtain the property of the first image.36. The system of claim 34, wherein processing the first image in theprojection domain to obtain the property comprises: processing the firstimage to obtain projection data that corresponds to the first image; andprocessing the projection data to obtain the property of the firstimage.